Many bridges built in the 1950s and 1960s in the United States are reaching their design life span. According to the ASCE (2009) Report Card bridges are commonly built to last 50 years and the average bridge in the United States is 43 years old. Furthermore, about 15% of the bridges are considered obsolete and are subjected to heavier loads than in the past because of the continuous increase in truck miles and carrying loads. Additionally the Committee on Fatigue and Fracture Reliability of the Committee on Structural Safety and Reliability of the Structural Division ASCE acknowledged that 80 to 90% of the failures in metallic structures are caused by a combination of fatigue and fracture (ASCE 1982). These statistics highlight the importance of condition assessment, specifically the monitoring of cracks in steel structural elements. Studies performed by the Federal Highway Administration show that bridge inspections are performed mainly through visual inspections with little use of other non-destructive techniques. Visual inspections are inexpensive, however are subjective and highly dependent of the experience of the inspector.
Many non-destructive techniques for the monitoring of crack growth have been proposed in the literature. X-rays, electric inspection, acoustic emission (AE), and dye penetrants have been utilized to assess the fatigue life of structural elements. AE is the most used non-destructive technique for monitoring bridges and other large structures.
AE uses a broad band of high frequency (ultrasonic 100 kHz to 1 MHz) sound waves typically Rayleigh (surface) and Lamb (plate) waves. These waves are created by a sudden release of energy, which can be either caused by a piezoelectric transducer (active AE) or events in the material such as phase transformation, grain boundary slip, and the growth of a crack (passive AE). Active ultrasound has been used for the detection and image of cracks on plates. An oscillatory voltage at the piezoelectric coupling sensor creates a Lamb wave that travels through the material and is affected by the surface cracks. The characteristics from the received signal allow locating and imaging cracks at the surface material. The methodology has been validated using different methods such as guided waves in pitch-catch, pulse-echo, or high frequency impedance spectrum method in different materials like steel, aluminum, and fiber reinforced polymers. Passive AE based on the transient elastic waves generated by the release of energy at crack growth has been proposed for the monitoring of cracks. It is known that a portion of the energy released at the instant of crack growth is dissipated in the form of elastic waves and heat. The premise is that AE features (e.g. number of counts that the signal is above some threshold level, absolute energy, or signal strength) are an indication of the energy released, which can be used to estimate the current state of the crack using fracture mechanics theory. In practice this is very challenging because there is not a theoretical relationship between AE features and the fracture mechanics parameters even though that studies suggest that AE rate increases as the crack growth rate increases. The relationships are difficult to apply to all structural element geometries, because each structural member of every bridge is different and most of them contain complex geometries. A methodology that estimates the stress intensity factor range without the need of knowing the structural member geometry would be appropriate for damage identification and prognosis of steel bridges.
Ideally, bridges should have a real-time condition assessment and monitoring system that monitors and estimates the probability of collapse of a particular structure based on the state of crack growth of critical structural members. Ideally, this process should be performed automatically and with as little human intervention as possible. The information should be transferred to bridge engineers to support decisions on maintenance and retrofit. However, the state of the art on crack growth monitoring using AE is not at that point yet, because the current relationships between sensed data and physical structural parameters are difficult to apply to operational bridges. In addition most of the Bayesian methodologies available in the literature are based on crack length measurements which are difficult to obtain.